Overview

Dataset statistics

Number of variables16
Number of observations93
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.0 KiB
Average record size in memory132.0 B

Variable types

Numeric13
Categorical3

Alerts

dbsscan_cluster has constant value ""Constant
dbscan_labels has constant value ""Constant
Alcohol is highly overall correlated with Color_Intensity and 4 other fieldsHigh correlation
Ash_Alcanity is highly overall correlated with Color_IntensityHigh correlation
Color_Intensity is highly overall correlated with Alcohol and 5 other fieldsHigh correlation
Flavanoids is highly overall correlated with Alcohol and 6 other fieldsHigh correlation
Magnesium is highly overall correlated with Alcohol and 4 other fieldsHigh correlation
Nonflavanoid_Phenols is highly overall correlated with kmeans_clusterHigh correlation
Proanthocyanins is highly overall correlated with Flavanoids and 1 other fieldsHigh correlation
Proline is highly overall correlated with Alcohol and 3 other fieldsHigh correlation
Total_Phenols is highly overall correlated with Alcohol and 4 other fieldsHigh correlation
kmeans_cluster is highly overall correlated with Flavanoids and 1 other fieldsHigh correlation
Malic_Acid has 1 (1.1%) zerosZeros
Ash has 2 (2.2%) zerosZeros
Ash_Alcanity has 1 (1.1%) zerosZeros
Magnesium has 1 (1.1%) zerosZeros
Color_Intensity has 1 (1.1%) zerosZeros
Proline has 1 (1.1%) zerosZeros

Reproduction

Analysis started2023-11-29 06:57:19.532627
Analysis finished2023-11-29 06:58:10.064326
Duration50.53 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

Alcohol
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52962649
Minimum0.1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-11-29T06:58:10.251308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.16210526
Q10.33157895
median0.54736842
Q30.71315789
95-th percentile0.84947368
Maximum1
Range0.9
Interquartile range (IQR)0.38157895

Descriptive statistics

Standard deviation0.2290867
Coefficient of variation (CV)0.43254389
Kurtosis-1.1157615
Mean0.52962649
Median Absolute Deviation (MAD)0.19473684
Skewness-0.12350573
Sum49.255263
Variance0.052480715
MonotonicityNot monotonic
2023-11-29T06:58:10.595224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5315789474 4
 
4.3%
0.3526315789 4
 
4.3%
0.2763157895 3
 
3.2%
0.3315789474 2
 
2.2%
0.5815789474 2
 
2.2%
0.4447368421 2
 
2.2%
0.7973684211 2
 
2.2%
0.2552631579 2
 
2.2%
0.7447368421 2
 
2.2%
0.8078947368 2
 
2.2%
Other values (65) 68
73.1%
ValueCountFrequency (%)
0.1 1
1.1%
0.1105263158 1
1.1%
0.1131578947 1
1.1%
0.1526315789 1
1.1%
0.1605263158 1
1.1%
0.1631578947 1
1.1%
0.1657894737 1
1.1%
0.2078947368 2
2.2%
0.2131578947 1
1.1%
0.2210526316 1
1.1%
ValueCountFrequency (%)
1 1
1.1%
0.8842105263 1
1.1%
0.8815789474 1
1.1%
0.8789473684 1
1.1%
0.8605263158 1
1.1%
0.8421052632 1
1.1%
0.8394736842 2
2.2%
0.8368421053 1
1.1%
0.8342105263 1
1.1%
0.8131578947 1
1.1%

Malic_Acid
Real number (ℝ)

ZEROS 

Distinct70
Distinct (%)75.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26884012
Minimum0
Maximum0.80224719
Zeros1
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-11-29T06:58:10.909411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.055280899
Q10.1752809
median0.22247191
Q30.28764045
95-th percentile0.70921348
Maximum0.80224719
Range0.80224719
Interquartile range (IQR)0.11235955

Descriptive statistics

Standard deviation0.17927508
Coefficient of variation (CV)0.66684644
Kurtosis2.0403336
Mean0.26884012
Median Absolute Deviation (MAD)0.051685393
Skewness1.5601626
Sum25.002132
Variance0.032139554
MonotonicityNot monotonic
2023-11-29T06:58:11.235161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2224719101 5
 
5.4%
0.2404494382 4
 
4.3%
0.208988764 3
 
3.2%
0.195505618 2
 
2.2%
0.08764044944 2
 
2.2%
0.204494382 2
 
2.2%
0.206741573 2
 
2.2%
0.1707865169 2
 
2.2%
0.2112359551 2
 
2.2%
0.2606741573 2
 
2.2%
Other values (60) 67
72.0%
ValueCountFrequency (%)
0 1
1.1%
0.03370786517 1
1.1%
0.03595505618 1
1.1%
0.0404494382 1
1.1%
0.05393258427 1
1.1%
0.05617977528 1
1.1%
0.0606741573 1
1.1%
0.08764044944 2
2.2%
0.09662921348 1
1.1%
0.1056179775 1
1.1%
ValueCountFrequency (%)
0.802247191 1
1.1%
0.8 1
1.1%
0.7415730337 1
1.1%
0.7303370787 1
1.1%
0.7280898876 1
1.1%
0.6966292135 1
1.1%
0.6876404494 1
1.1%
0.6741573034 1
1.1%
0.6404494382 1
1.1%
0.604494382 1
1.1%

Ash
Real number (ℝ)

ZEROS 

Distinct57
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51322052
Minimum0
Maximum0.95901639
Zeros2
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-11-29T06:58:11.586918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.16721311
Q10.36065574
median0.5
Q30.68032787
95-th percentile0.81967213
Maximum0.95901639
Range0.95901639
Interquartile range (IQR)0.31967213

Descriptive statistics

Standard deviation0.22148311
Coefficient of variation (CV)0.43155544
Kurtosis-0.48333452
Mean0.51322052
Median Absolute Deviation (MAD)0.1557377
Skewness-0.25981162
Sum47.729508
Variance0.049054766
MonotonicityNot monotonic
2023-11-29T06:58:11.905193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4918032787 4
 
4.3%
0.3852459016 3
 
3.2%
0.6229508197 3
 
3.2%
0.8196721311 3
 
3.2%
0.1803278689 3
 
3.2%
0.4754098361 3
 
3.2%
0.3442622951 3
 
3.2%
0.5409836066 3
 
3.2%
0.3278688525 2
 
2.2%
0.6639344262 2
 
2.2%
Other values (47) 64
68.8%
ValueCountFrequency (%)
0 2
2.2%
0.008196721311 1
 
1.1%
0.09836065574 1
 
1.1%
0.1475409836 1
 
1.1%
0.1803278689 3
3.2%
0.1967213115 1
 
1.1%
0.2049180328 1
 
1.1%
0.2295081967 2
2.2%
0.237704918 1
 
1.1%
0.2459016393 2
2.2%
ValueCountFrequency (%)
0.9590163934 1
 
1.1%
0.9344262295 1
 
1.1%
0.9016393443 1
 
1.1%
0.8360655738 1
 
1.1%
0.8196721311 3
3.2%
0.8032786885 2
2.2%
0.7950819672 2
2.2%
0.7786885246 2
2.2%
0.7540983607 2
2.2%
0.7459016393 2
2.2%

Ash_Alcanity
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.46112848
Minimum0
Maximum0.93670886
Zeros1
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-11-29T06:58:12.205116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.22278481
Q10.32278481
median0.46835443
Q30.55696203
95-th percentile0.79493671
Maximum0.93670886
Range0.93670886
Interquartile range (IQR)0.23417722

Descriptive statistics

Standard deviation0.17745576
Coefficient of variation (CV)0.38482931
Kurtosis0.19117554
Mean0.46112848
Median Absolute Deviation (MAD)0.12025316
Skewness0.27322797
Sum42.884948
Variance0.031490545
MonotonicityNot monotonic
2023-11-29T06:58:12.510807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.3037974684 9
 
9.7%
0.4936708861 8
 
8.6%
0.6202531646 6
 
6.5%
0.4303797468 6
 
6.5%
0.5569620253 4
 
4.3%
0.3797468354 3
 
3.2%
0.7151898734 3
 
3.2%
0.4810126582 3
 
3.2%
0.5886075949 3
 
3.2%
0.3544303797 3
 
3.2%
Other values (38) 45
48.4%
ValueCountFrequency (%)
0 1
1.1%
0.07594936709 1
1.1%
0.1265822785 1
1.1%
0.1772151899 1
1.1%
0.2151898734 1
1.1%
0.2278481013 1
1.1%
0.2405063291 1
1.1%
0.253164557 2
2.2%
0.2721518987 2
2.2%
0.2784810127 1
1.1%
ValueCountFrequency (%)
0.9367088608 1
 
1.1%
0.8734177215 1
 
1.1%
0.8417721519 1
 
1.1%
0.8101265823 2
2.2%
0.7848101266 1
 
1.1%
0.7341772152 1
 
1.1%
0.7151898734 3
3.2%
0.6835443038 1
 
1.1%
0.6582278481 1
 
1.1%
0.6518987342 1
 
1.1%

Magnesium
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)45.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.43682216
Minimum0
Maximum0.90625
Zeros1
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-11-29T06:58:12.805982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.15625
Q10.28125
median0.421875
Q30.546875
95-th percentile0.7625
Maximum0.90625
Range0.90625
Interquartile range (IQR)0.265625

Descriptive statistics

Standard deviation0.19180348
Coefficient of variation (CV)0.43908826
Kurtosis-0.24344016
Mean0.43682216
Median Absolute Deviation (MAD)0.140625
Skewness0.40449631
Sum40.624461
Variance0.036788576
MonotonicityNot monotonic
2023-11-29T06:58:13.172275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0.25 7
 
7.5%
0.4375 5
 
5.4%
0.28125 5
 
5.4%
0.375 5
 
5.4%
0.40625 4
 
4.3%
0.484375 4
 
4.3%
0.75 3
 
3.2%
0.421875 3
 
3.2%
0.578125 3
 
3.2%
0.234375 3
 
3.2%
Other values (32) 51
54.8%
ValueCountFrequency (%)
0 1
 
1.1%
0.125 3
3.2%
0.15625 2
 
2.2%
0.1875 1
 
1.1%
0.21875 2
 
2.2%
0.234375 3
3.2%
0.25 7
7.5%
0.265625 3
3.2%
0.28125 5
5.4%
0.296875 1
 
1.1%
ValueCountFrequency (%)
0.90625 1
 
1.1%
0.890625 1
 
1.1%
0.875 1
 
1.1%
0.796875 1
 
1.1%
0.78125 1
 
1.1%
0.75 3
3.2%
0.734375 1
 
1.1%
0.71875 2
2.2%
0.703125 1
 
1.1%
0.671875 1
 
1.1%

Total_Phenols
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54453096
Minimum0.13793103
Maximum0.98965517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-11-29T06:58:13.590492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.13793103
5-th percentile0.31241379
Q10.42758621
median0.55862069
Q30.64827586
95-th percentile0.77241379
Maximum0.98965517
Range0.85172414
Interquartile range (IQR)0.22068966

Descriptive statistics

Standard deviation0.15283316
Coefficient of variation (CV)0.28066936
Kurtosis0.15716543
Mean0.54453096
Median Absolute Deviation (MAD)0.10344828
Skewness-0.078305495
Sum50.641379
Variance0.023357973
MonotonicityNot monotonic
2023-11-29T06:58:14.065931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4206896552 6
 
6.5%
0.6965517241 6
 
6.5%
0.6275862069 5
 
5.4%
0.5586206897 5
 
5.4%
0.5068965517 4
 
4.3%
0.3517241379 3
 
3.2%
0.524137931 3
 
3.2%
0.6793103448 3
 
3.2%
0.6448275862 3
 
3.2%
0.4965517241 3
 
3.2%
Other values (43) 52
55.9%
ValueCountFrequency (%)
0.1379310345 1
1.1%
0.2172413793 1
1.1%
0.2310344828 1
1.1%
0.275862069 1
1.1%
0.3103448276 1
1.1%
0.3137931034 1
1.1%
0.3172413793 1
1.1%
0.324137931 1
1.1%
0.3344827586 2
2.2%
0.3448275862 1
1.1%
ValueCountFrequency (%)
0.9896551724 1
 
1.1%
0.8689655172 1
 
1.1%
0.8 1
 
1.1%
0.7896551724 1
 
1.1%
0.7827586207 1
 
1.1%
0.7655172414 1
 
1.1%
0.7586206897 1
 
1.1%
0.7482758621 1
 
1.1%
0.7310344828 1
 
1.1%
0.6965517241 6
6.5%

Flavanoids
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)78.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4661313
Minimum0.25949367
Maximum0.66455696
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-11-29T06:58:14.610821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.25949367
5-th percentile0.27172996
Q10.37341772
median0.48734177
Q30.55696203
95-th percentile0.61983122
Maximum0.66455696
Range0.40506329
Interquartile range (IQR)0.1835443

Descriptive statistics

Standard deviation0.11240312
Coefficient of variation (CV)0.24114046
Kurtosis-1.0083772
Mean0.4661313
Median Absolute Deviation (MAD)0.082278481
Skewness-0.31314517
Sum43.350211
Variance0.012634461
MonotonicityNot monotonic
2023-11-29T06:58:15.732299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4873417722 4
 
4.3%
0.2742616034 2
 
2.2%
0.4936708861 2
 
2.2%
0.2848101266 2
 
2.2%
0.2974683544 2
 
2.2%
0.3565400844 2
 
2.2%
0.4071729958 2
 
2.2%
0.5611814346 2
 
2.2%
0.5443037975 2
 
2.2%
0.5970464135 2
 
2.2%
Other values (63) 71
76.3%
ValueCountFrequency (%)
0.2594936709 1
1.1%
0.2616033755 1
1.1%
0.2637130802 1
1.1%
0.2658227848 1
1.1%
0.2679324895 1
1.1%
0.2742616034 2
2.2%
0.2848101266 2
2.2%
0.2974683544 2
2.2%
0.2995780591 1
1.1%
0.305907173 1
1.1%
ValueCountFrequency (%)
0.664556962 1
1.1%
0.6434599156 2
2.2%
0.6286919831 1
1.1%
0.6223628692 1
1.1%
0.6181434599 1
1.1%
0.6139240506 1
1.1%
0.611814346 1
1.1%
0.6097046414 1
1.1%
0.6012658228 1
1.1%
0.5991561181 1
1.1%

Nonflavanoid_Phenols
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.32318929
Minimum0.075471698
Maximum0.66037736
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-11-29T06:58:16.203353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.075471698
5-th percentile0.14339623
Q10.24528302
median0.30188679
Q30.39622642
95-th percentile0.54716981
Maximum0.66037736
Range0.58490566
Interquartile range (IQR)0.1509434

Descriptive statistics

Standard deviation0.13108222
Coefficient of variation (CV)0.40558962
Kurtosis-0.18451666
Mean0.32318929
Median Absolute Deviation (MAD)0.094339623
Skewness0.47833189
Sum30.056604
Variance0.017182548
MonotonicityNot monotonic
2023-11-29T06:58:16.668141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0.2452830189 10
 
10.8%
0.320754717 8
 
8.6%
0.3962264151 7
 
7.5%
0.3018867925 7
 
7.5%
0.2641509434 6
 
6.5%
0.358490566 6
 
6.5%
0.4528301887 6
 
6.5%
0.1509433962 5
 
5.4%
0.1698113208 5
 
5.4%
0.5471698113 4
 
4.3%
Other values (14) 29
31.2%
ValueCountFrequency (%)
0.07547169811 2
 
2.2%
0.1132075472 1
 
1.1%
0.1320754717 2
 
2.2%
0.1509433962 5
5.4%
0.1698113208 5
5.4%
0.2075471698 4
 
4.3%
0.2264150943 2
 
2.2%
0.2452830189 10
10.8%
0.2641509434 6
6.5%
0.2830188679 4
 
4.3%
ValueCountFrequency (%)
0.6603773585 2
 
2.2%
0.6037735849 1
 
1.1%
0.5660377358 1
 
1.1%
0.5471698113 4
4.3%
0.5094339623 3
3.2%
0.4905660377 3
3.2%
0.4528301887 6
6.5%
0.4150943396 1
 
1.1%
0.3962264151 7
7.5%
0.3773584906 1
 
1.1%

Proanthocyanins
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53248999
Minimum0.1254902
Maximum0.98039216
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-11-29T06:58:17.162112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1254902
5-th percentile0.30666667
Q10.40392157
median0.50196078
Q30.62745098
95-th percentile0.80627451
Maximum0.98039216
Range0.85490196
Interquartile range (IQR)0.22352941

Descriptive statistics

Standard deviation0.17026589
Coefficient of variation (CV)0.31975416
Kurtosis0.15149962
Mean0.53248999
Median Absolute Deviation (MAD)0.11372549
Skewness0.47013194
Sum49.521569
Variance0.028990472
MonotonicityNot monotonic
2023-11-29T06:58:17.616564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4901960784 4
 
4.3%
0.6156862745 4
 
4.3%
0.4117647059 4
 
4.3%
0.368627451 4
 
4.3%
0.6549019608 3
 
3.2%
0.6117647059 3
 
3.2%
0.7725490196 3
 
3.2%
0.5725490196 3
 
3.2%
0.737254902 2
 
2.2%
0.6352941176 2
 
2.2%
Other values (52) 61
65.6%
ValueCountFrequency (%)
0.1254901961 1
 
1.1%
0.2431372549 2
2.2%
0.2470588235 1
 
1.1%
0.2901960784 1
 
1.1%
0.3176470588 1
 
1.1%
0.3294117647 2
2.2%
0.3411764706 1
 
1.1%
0.3529411765 1
 
1.1%
0.3647058824 1
 
1.1%
0.368627451 4
4.3%
ValueCountFrequency (%)
0.9803921569 2
2.2%
0.9411764706 1
 
1.1%
0.9215686275 1
 
1.1%
0.8156862745 1
 
1.1%
0.8 1
 
1.1%
0.7725490196 3
3.2%
0.7607843137 1
 
1.1%
0.7568627451 1
 
1.1%
0.737254902 2
2.2%
0.7333333333 1
 
1.1%

Color_Intensity
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct75
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31871713
Minimum0
Maximum0.70562771
Zeros1
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-11-29T06:58:17.913824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.084415584
Q10.17532468
median0.32142857
Q30.42965368
95-th percentile0.59848485
Maximum0.70562771
Range0.70562771
Interquartile range (IQR)0.254329

Descriptive statistics

Standard deviation0.16295593
Coefficient of variation (CV)0.51128701
Kurtosis-0.76075216
Mean0.31871713
Median Absolute Deviation (MAD)0.14177489
Skewness0.25750303
Sum29.640693
Variance0.026554634
MonotonicityNot monotonic
2023-11-29T06:58:18.228456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1428571429 4
 
4.3%
0.1645021645 3
 
3.2%
0.3593073593 3
 
3.2%
0.2727272727 3
 
3.2%
0.4134199134 3
 
3.2%
0.4675324675 2
 
2.2%
0.5324675325 2
 
2.2%
0.3484848485 2
 
2.2%
0.1753246753 2
 
2.2%
0.2294372294 2
 
2.2%
Other values (65) 67
72.0%
ValueCountFrequency (%)
0 1
1.1%
0.04978354978 1
1.1%
0.07251082251 1
1.1%
0.07792207792 1
1.1%
0.08441558442 2
2.2%
0.09090909091 1
1.1%
0.09415584416 1
1.1%
0.09956709957 1
1.1%
0.1266233766 1
1.1%
0.132034632 1
1.1%
ValueCountFrequency (%)
0.7056277056 1
1.1%
0.6515151515 1
1.1%
0.6428571429 1
1.1%
0.6406926407 1
1.1%
0.6082251082 1
1.1%
0.591991342 1
1.1%
0.5519480519 1
1.1%
0.5432900433 1
1.1%
0.5378787879 1
1.1%
0.5324675325 2
2.2%

Hue
Real number (ℝ)

Distinct47
Distinct (%)50.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.59660791
Minimum0.25773196
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-11-29T06:58:18.541960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.25773196
5-th percentile0.3257732
Q10.4742268
median0.59793814
Q30.70103093
95-th percentile0.86391753
Maximum1
Range0.74226804
Interquartile range (IQR)0.22680412

Descriptive statistics

Standard deviation0.16279285
Coefficient of variation (CV)0.27286405
Kurtosis-0.40381945
Mean0.59660791
Median Absolute Deviation (MAD)0.11340206
Skewness0.068420587
Sum55.484536
Variance0.026501513
MonotonicityNot monotonic
2023-11-29T06:58:18.852751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0.5773195876 6
 
6.5%
0.7731958763 5
 
5.4%
0.6597938144 5
 
5.4%
0.6082474227 4
 
4.3%
0.6288659794 4
 
4.3%
0.3917525773 3
 
3.2%
0.7319587629 3
 
3.2%
0.5979381443 3
 
3.2%
0.4432989691 2
 
2.2%
0.412371134 2
 
2.2%
Other values (37) 56
60.2%
ValueCountFrequency (%)
0.2577319588 1
 
1.1%
0.2783505155 2
2.2%
0.3195876289 2
2.2%
0.3298969072 2
2.2%
0.3505154639 1
 
1.1%
0.3917525773 3
3.2%
0.4020618557 1
 
1.1%
0.412371134 2
2.2%
0.4226804124 2
2.2%
0.4329896907 1
 
1.1%
ValueCountFrequency (%)
1 1
 
1.1%
0.9278350515 1
 
1.1%
0.9072164948 2
 
2.2%
0.8762886598 1
 
1.1%
0.8556701031 2
 
2.2%
0.824742268 1
 
1.1%
0.8144329897 1
 
1.1%
0.793814433 2
 
2.2%
0.7835051546 1
 
1.1%
0.7731958763 5
5.4%

OD280
Real number (ℝ)

Distinct72
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.65587459
Minimum0.31868132
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-11-29T06:58:19.158207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.31868132
5-th percentile0.38095238
Q10.55311355
median0.66300366
Q30.76923077
95-th percentile0.86593407
Maximum1
Range0.68131868
Interquartile range (IQR)0.21611722

Descriptive statistics

Standard deviation0.14870109
Coefficient of variation (CV)0.22672183
Kurtosis-0.44095278
Mean0.65587459
Median Absolute Deviation (MAD)0.10989011
Skewness-0.12973339
Sum60.996337
Variance0.022112013
MonotonicityNot monotonic
2023-11-29T06:58:19.474019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5531135531 4
 
4.3%
0.695970696 3
 
3.2%
0.5860805861 3
 
3.2%
0.7545787546 3
 
3.2%
0.7106227106 2
 
2.2%
0.6080586081 2
 
2.2%
0.5787545788 2
 
2.2%
0.8461538462 2
 
2.2%
0.6923076923 2
 
2.2%
0.619047619 2
 
2.2%
Other values (62) 68
73.1%
ValueCountFrequency (%)
0.3186813187 1
1.1%
0.3516483516 1
1.1%
0.3626373626 1
1.1%
0.3772893773 1
1.1%
0.380952381 2
2.2%
0.4212454212 1
1.1%
0.4285714286 1
1.1%
0.4358974359 1
1.1%
0.4432234432 1
1.1%
0.4542124542 1
1.1%
ValueCountFrequency (%)
1 1
1.1%
0.9706959707 1
1.1%
0.9340659341 1
1.1%
0.8937728938 1
1.1%
0.8681318681 1
1.1%
0.8644688645 1
1.1%
0.8498168498 1
1.1%
0.8461538462 2
2.2%
0.8424908425 1
1.1%
0.8351648352 1
1.1%

Proline
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct75
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38613808
Minimum0
Maximum0.88231098
Zeros1
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-11-29T06:58:19.799121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.046932953
Q10.15977175
median0.35805991
Q30.56134094
95-th percentile0.75891583
Maximum0.88231098
Range0.88231098
Interquartile range (IQR)0.40156919

Descriptive statistics

Standard deviation0.24510013
Coefficient of variation (CV)0.63474737
Kurtosis-1.1159596
Mean0.38613808
Median Absolute Deviation (MAD)0.20328103
Skewness0.200455
Sum35.910842
Variance0.060074076
MonotonicityNot monotonic
2023-11-29T06:58:20.111772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2867332382 3
 
3.2%
0.5399429387 3
 
3.2%
0.5613409415 2
 
2.2%
0.4044222539 2
 
2.2%
0.1654778887 2
 
2.2%
0.5577746077 2
 
2.2%
0.5042796006 2
 
2.2%
0.1069900143 2
 
2.2%
0.1547788873 2
 
2.2%
0.2025677603 2
 
2.2%
Other values (65) 71
76.3%
ValueCountFrequency (%)
0 1
1.1%
0.008559201141 1
1.1%
0.0242510699 1
1.1%
0.0335235378 1
1.1%
0.04564907275 1
1.1%
0.04778887304 1
1.1%
0.05492154066 1
1.1%
0.07132667618 1
1.1%
0.0727532097 2
2.2%
0.08131241084 1
1.1%
ValueCountFrequency (%)
0.8823109843 1
1.1%
0.8787446505 1
1.1%
0.8573466476 1
1.1%
0.8359486448 1
1.1%
0.7824536377 1
1.1%
0.7432239658 1
1.1%
0.7360912981 1
1.1%
0.7253922967 1
1.1%
0.7218259629 1
1.1%
0.7182596291 2
2.2%

kmeans_cluster
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
82 
1
11 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters93
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 82
88.2%
1 11
 
11.8%

Length

2023-11-29T06:58:20.379697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-29T06:58:20.629181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 82
88.2%
1 11
 
11.8%

Most occurring characters

ValueCountFrequency (%)
0 82
88.2%
1 11
 
11.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 93
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 82
88.2%
1 11
 
11.8%

Most occurring scripts

ValueCountFrequency (%)
Common 93
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 82
88.2%
1 11
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 82
88.2%
1 11
 
11.8%

dbsscan_cluster
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
93 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters93
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 93
100.0%

Length

2023-11-29T06:58:20.826259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-29T06:58:21.082504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 93
100.0%

Most occurring characters

ValueCountFrequency (%)
0 93
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 93
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 93
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 93
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 93
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 93
100.0%

dbscan_labels
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
93 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters93
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 93
100.0%

Length

2023-11-29T06:58:21.271814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-29T06:58:21.523026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 93
100.0%

Most occurring characters

ValueCountFrequency (%)
0 93
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 93
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 93
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 93
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 93
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 93
100.0%

Interactions

2023-11-29T06:58:06.128761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:20.489758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:25.613592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:29.085920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:33.542579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:36.952138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:39.856596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:42.968876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:48.145183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:51.160792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:54.216643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:57.386390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:02.374449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:06.333441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:20.874028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:25.871323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:29.352908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:33.769467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:37.162827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:40.087442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:43.189904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:48.348845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:51.410370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:54.444960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:57.609297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:02.751898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:06.590769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:21.307120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:26.142965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:29.677912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:34.021723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:37.406774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:40.345477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:43.501524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:48.598823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:51.654190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:54.707334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:57.895130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:03.027796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:06.800081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:21.702329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:26.380429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:30.031054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:34.615909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:37.630527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:40.577187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:43.864827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:48.828081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:51.891791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:54.940958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:58.188659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:03.285257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:07.014288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:21.981494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:26.623599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:30.409189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:34.831542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:37.834680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:40.829726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:44.243022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:49.037393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:52.087116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:55.164609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:59.182297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:03.517503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:07.219604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:22.190823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:26.872529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:30.755980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:35.055160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:38.031444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:41.054346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:44.604819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:49.238247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:52.310031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:55.390725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:59.553570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:03.742990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:07.435968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:22.419139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:27.117972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:31.075847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:35.276388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:38.233355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:41.287971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:45.477495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:49.451499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:52.530865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:55.619099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:59.876093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:03.964489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:07.686141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:22.692003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:27.391376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:31.477470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:35.517716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:38.481629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:41.561338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:45.880955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:49.696223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:52.793171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:55.883096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:00.234353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:04.216707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:07.907123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:22.908875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:27.647480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:31.826727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:35.733014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:38.697006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:41.805933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:46.315399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:49.923554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:53.036751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:56.127746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:00.567061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:04.857312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:08.128754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:24.638661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:27.891100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:32.080802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:35.933281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:38.893609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:42.037549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:46.695001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:50.127428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:53.267420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:56.345760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:00.894150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:05.147559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:08.374616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:24.900164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:28.145006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:32.465935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:36.179430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:39.125245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:42.279393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:47.094153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:50.379378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:53.525123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:56.595251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:01.289634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:05.406401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:08.633170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:25.160977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:28.428020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:32.849482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:36.477989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:39.391872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:42.530125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:47.430233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:50.648905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:53.758639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:56.847866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:01.631177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:05.655470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:08.877224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:25.407083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:28.803203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:33.273140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:36.728592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:39.636981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:42.767118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:47.837722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:50.925633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:54.011257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:57:57.128942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:02.024993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:58:05.911525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-11-29T06:58:21.729485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
AlcoholAshAsh_AlcanityColor_IntensityFlavanoidsHueMagnesiumMalic_AcidNonflavanoid_PhenolsOD280ProanthocyaninsProlineTotal_Phenolskmeans_cluster
Alcohol1.0000.281-0.4930.7830.6370.0530.5990.177-0.3480.2900.3220.7170.5250.431
Ash0.2811.0000.1550.2840.3040.1090.4110.1800.1090.1290.1390.4100.2750.000
Ash_Alcanity-0.4930.1551.000-0.581-0.423-0.077-0.3930.0140.363-0.171-0.250-0.470-0.3050.498
Color_Intensity0.7830.284-0.5811.0000.7620.0400.6070.118-0.4070.2060.4130.7740.6180.489
Flavanoids0.6370.304-0.4230.7621.000-0.0840.5380.221-0.4950.3560.6390.5730.8750.806
Hue0.0530.109-0.0770.040-0.0841.000-0.036-0.4410.024-0.249-0.1420.086-0.1730.348
Magnesium0.5990.411-0.3930.6070.538-0.0361.0000.272-0.1730.1800.2610.5670.5290.388
Malic_Acid0.1770.1800.0140.1180.221-0.4410.2721.000-0.0330.2650.0400.2130.2710.000
Nonflavanoid_Phenols-0.3480.1090.363-0.407-0.4950.024-0.173-0.0331.000-0.423-0.355-0.242-0.4400.742
OD2800.2900.129-0.1710.2060.356-0.2490.1800.265-0.4231.0000.2660.1720.3900.302
Proanthocyanins0.3220.139-0.2500.4130.639-0.1420.2610.040-0.3550.2661.0000.2560.5120.141
Proline0.7170.410-0.4700.7740.5730.0860.5670.213-0.2420.1720.2561.0000.4540.463
Total_Phenols0.5250.275-0.3050.6180.875-0.1730.5290.271-0.4400.3900.5120.4541.0000.431
kmeans_cluster0.4310.0000.4980.4890.8060.3480.3880.0000.7420.3020.1410.4630.4311.000

Missing values

2023-11-29T06:58:09.235967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-29T06:58:09.807036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AlcoholMalic_AcidAshAsh_AlcanityMagnesiumTotal_PhenolsFlavanoidsNonflavanoid_PhenolsProanthocyaninsColor_IntensityHueOD280Prolinekmeans_clusterdbsscan_clusterdbscan_labels
00.8421050.2179780.5983610.2784810.8906250.6275860.5738400.2830190.7372550.4718610.5773200.9706960.561341000
10.5710530.2337080.3606560.0000000.4687500.5758620.5105490.2452830.3411760.3354980.5876290.7802200.550642000
20.5605260.3640450.7950820.4683540.4843750.6275860.6118140.3207550.9411760.4761900.5670100.6959710.646933000
30.8789470.2719100.6557380.3544300.6718750.9896550.6645570.2075470.6941180.7056280.3917530.7985350.857347000
40.5815790.4157300.9590160.6202530.7500000.6275860.4957810.4905660.5529410.3290040.5773200.6080590.325963000
50.8342110.2292130.6147540.2531650.6562500.7896550.6434600.3962260.6117650.5919910.5876290.5787550.835949000
60.8842110.2539330.6147540.2151900.4062500.5241380.4599160.3207550.6156860.4296540.5567010.8461540.721826000
70.7973680.3168540.7459020.4050630.7968750.5586210.4578060.3396230.3294120.4080090.5979380.8461540.725392000
81.0000000.2022470.3852460.1772150.4218750.6275860.5569620.3018870.6156860.4242420.6185570.5787550.547076000
90.7447370.1370790.4672130.3037970.4375000.6896550.5928270.1698110.5647060.6428570.5463920.8351650.547076000
AlcoholMalic_AcidAshAsh_AlcanityMagnesiumTotal_PhenolsFlavanoidsNonflavanoid_PhenolsProanthocyaninsColor_IntensityHueOD280Prolinekmeans_clusterdbsscan_clusterdbscan_labels
1130.1000000.0000000.6557380.6202530.2812500.5172410.3523210.5471700.4039220.1948050.6391750.3809520.111270100
1160.2078950.1640450.2377050.6075950.2500000.3448280.2658230.3207550.4392160.0725110.4845360.7545790.154779000
1170.3657890.1955060.4016390.7151900.5937500.3517240.3691980.3962260.4705880.0844160.5979380.6190480.047789000
1190.2552630.6044940.2459020.4936710.2656250.3517240.2742620.4528300.5725490.0000000.4639180.6520150.203994100
1200.1105260.3730340.5901640.5569620.4062500.6620690.5168780.3584910.5568630.2132030.3298970.7765570.247504000
1230.5315790.3459520.3524590.6518990.2500000.5655170.4873420.3207550.6274510.1428570.2577320.6703300.072753000
1240.2210530.8022470.5655740.6202530.1875000.6482760.5675110.1509430.9803920.1645020.2783510.8681320.072753000
1250.2736840.3191010.3852460.6202530.2343750.5586210.4873420.4528300.3686270.1601730.3917530.7362640.071327000
1280.3526320.2000000.4918030.8417720.2812500.4275860.4451480.5094340.5843140.0909090.4226800.5531140.045649000
1290.2657890.8000000.5573770.6835440.1562500.3862070.2974680.5471700.3686270.1428570.3195880.4761900.215407100